Spectral filtering is a very useful optical function that can be utilized to control the temporal waveform of pulsed optical signals, cross-correlate or otherwise process optical signals, and to differentially control and manipulate spectrally-distinguished optical communication channels, as found for example in wave-division-multiplexed (WDM) optical communication systems. Devices have been introduced over the years to perform spectral filtering, all of which have characteristic shortcomings along with their strengths. In many cases these shortcomings, including limited spectral resolution, alignment sensitivity, fabrication difficulties, high cost, and lack of flexibility, have prevented widespread application.
A spectral filtering device, according to the present usage, is a device that applies a fixed or dynamically re-programmable, complex-valued, spectral transfer function to an input signal. If Ein(ω) and Eout(ω), respectively, represent Fourier spectra of input and output signals, computed on the basis of the time-varying electric fields of the two signals, and T(ω) is a complex-valued spectral transfer function of modulus unity or smaller, the effect of the spectral filtering device (also called an optical processing device, OPD) can be represented as
 Eout(ω)=T(ω) Ein(ω)  (1)
The transfer function T(ω) has an overall width Δω and a resolution width Δr, where the latter quantity is the minimum spectral interval over which T(ω) displays variation (see FIG. 1), and is a significant measure of the transformation ability of a spectral filtering device. The physical characteristics of a particular spectral filtering device determine the range and types of spectral transfer functions that it can be configured to provide. We limit our discussion here to spectral filtering devices that act to apply a fully coherent transfer function, i.e. the device fully controls the amplitude and phase shifts applied to the input signal spectrum, except for an overall phase factor.
Spectral filtering devices can be utilized to transform input signals from one format into another, or to tailor their spectra to some preferred form. A spectral filtering device, according to the present usage, may or may not have the additional capacity to transform the spatial wavefront of input optical signals.
As a special case, if T(ω) is set equal to the conjugate Fourier spectrum Eref(ω) of a reference temporal waveform, also called the design temporal waveform, the output field from the spectral filtering device is proportional to the cross-correlation of the input field with the reference temporal waveform. Temporal cross-correlation capability is widely useful in temporal pattern recognition.
The capabilities of a spectral filtering device can be utilized in multiple ways in communications systems, including signal coding and decoding for Code-Division Multiplexing (CDM), optical packet recognition, code-based contention resolution, as WDM multiplexers and demultiplexers, and as WDM add/drop multiplexers. FIG. 2 depicts the encoding and decoding of optical signals in a CDM context. Data 202 is input through a first communication channel, and data 206 is input through a second communication channel. Data 202 passes through a spectral filter 204, which encodes data 202 with an identifying code. Similarly, data 206 is encoded with an identifying code by a spectral filter 208. The encoded signals are combined and transmitted over an optical transmission line 210. At their destination the encoded signals are split into two paths, 212 and 214. The upper path 212 feeds into a spectral filter 216, which imparts a transfer function that is the conjugate transfer function of the filter 204. The output of spectral filter 216 is a signal comprising the superposition of data 202 and data 206; however, due to the encoding imparted by spectral filters 204 and 208 and subsequent decoding by spectral filter 216, this output signal contains a component 218 originating from 202 that has a specific recognizable temporal waveform, typically comprising a brief high power peak for each bit transmitted, along with a component 220 originating from data 206. In the upper path, the component originating from data 206 has a temporal waveform structure that can be discriminated against in detection. Typically, component 220 has no brief high power peak.
In similar fashion, the lower branch 214 feeds into a spectral filter 222, the output of which is a signal made up of the superposition of a component 224 originating from data 206, and a component 226 originating from signal 202. As before, the two signal components have distinguishable temporal waveforms, with the component from data 206 typically having a brief detactable high power peak while the component from data 202 lacking the brief high power peak, and hence remaining below a detection threshold. A key element in CDM detection is the implementation of thresholding in the detection scheme that can distinguish input pulses of differing temporal waveform character.
A variety of other CDM methods are known and, many of them having need for high performance spectral filtering devices. Some alternative CDM approaches operate entirely with spectral coding. Different applications for high performance spectral filtering devices exist. For example, spectral filtering devices capable of accepting multiple wavelength-distinguished communication channels through a particular input port, and parsing the channels in a predetermined fashion to a set of output ports, i.e., a WDM demultiplexer, have wide application. This is especially true if the spectral filtering device is capable of handling arbitrary spectral channel spacing with flexible and controllable spectral bandpass functions.
A widely known approach to implementing coherent spectral filtering is a dual-grating, free-space optical design, shown schematically in FIG. 3. Gratings 302 and 310 are periodic, with grooves of constant spacing and amplitude. A first grating 302 spectrally disperses an input signal, providing a mapping of frequency-to-position along the x-direction of the filter plane. A lens 304 directs the signal to a planar phase and/or amplitude mask 306, varying in the x-direction, with Δr representing the minimum spectral width over which the mask exhibits variation. A second lens 308 directs the output of the mask 306 to a second grating 310, which accepts the filtered spectral components that have passed through the mask 306, and maps them onto a common output direction.
The dual-grating, free-space spectral filtering device has limited appeal in the context of communication systems because of its physical complexity, sensitivity to precision alignment, relatively large insertion loss, and limited spectral resolution for gratings of tractable physical dimensions. In the dual-grating spectral filter described above, the gratings act only to apply and invert an angle-to-space mapping; no information specific to the transfer function to be imparted resides in the gratings. The mask 306 is necessary to impart the transfer function.
There is another class of spectral filters wherein the entire spectral filtering function is effected through diffraction from a single diffractive structure, having diffractive elements whose diffractive amplitudes, optical spacings, or spatial phases vary along some design spatial dimension of the grating. Diffractive elements correspond, for example, to individual grooves of a diffraction grating, or individual periods of refractive index variation in a volume index grating. Diffractive amplitude refers to the amplitude of the diffracted signal produced by a particular diffraction element, and may be controlled by groove depth, magnitude of refractive index variation, magnitude of absorption, or other quantity, depending on the specific type of diffractive elements comprising the diffractive structure under consideration. Optical separation of diffractive elements refers to the optical path difference between diffractive elements. Spatial phase refers to the positioning as a function of optical path length of diffractive elements relative to a periodic reference waveform. The spatial variation of the diffractive elements encodes all aspects of the transfer function to be applied. We refer here to diffractive structures whose diffractive elements (grooves, lines, planes, refractive-index contours, etc.) possess spatial variation representative of a specific spectral transfer function using the term “programmed.” Programmed diffractive structures, i.e. those whose diffraction elements possess spatial structure that encodes a desired spectral transfer function, have only been previously disclosed in the case of surface relief gratings, and in fiber gratings whose diffractive elements correspond to lines (or grooves) and constant index planes, respectively. Programmed diffractive structures known in the art do not provide for the implementation of general wavefront transformations simultaneously with general spectral transformations.
Programmed surface gratings and programmed fiber gratings are encumbered with severe functional constraints. A programmed surface-grating filter has a fundamentally low efficiency, and requires alignment sensitive free-space optical elements to function. Programmed fiber-grating filters produce output signals that are difficult to separate from input signals (since they can only co- or counterpropagate), and can only support a single transfer function within a given spectral window.
In 1998, Babbitt and Mossberg [(Opt. Commun. 148, 23 (1998)] introduced a programmed surface-grating filter, either reflective or transmissive, whose diffractive elements (straight grooves) exhibit spatial structure, i.e., variations in diffractive amplitude, optical separation, or spatial phase, in the direction perpendicular to their length. A free-space implementation 400 of this device is schematically represented in FIG. 4. The diffractive elements (grooves) of the programmed surface-grating filter extend uniformly normal to the plane of the figure, while the diffractive amplitude, spatial separation, and/or spatial phase of the diffractive elements varies with position along the x-direction. A programmed surface-grating device can be produced by a variety of fast and economical processes such as by stamping, lithography, or masking processes. However, programmed surface-grating filters have a very serious deficiency in their intrinsically low efficiency. The profile of a programmed surface grating can be thought of as an assemblage of sine gratings, each of which maps one spectral component of the input signal to the output direction. Since the surface diffraction condition constrains only the surface projection of the input and output wavevectors, however, each constitutive sine grating interacts with all spectral components of the input beam, diffracting all but its design component into discarded output directions. As a result, the higher the complexity of the programmed transfer function (and therefore, the more sine components needed to describe it), the lower the efficiency of the programmed surface grating filter.
Fiber Bragg gratings, such as 502 illustrated in FIG. 5, have become an accepted component in optical communications systems. Programmed fiber Bragg gratings have been disclosed, and provide for higher efficiency and easier implementation than programmed surface gratings. Programmed fiber-grating filters are implemented in fiber links in the same manner as ordinary fiber-grating devices, typically using a circulator 504. Programmed fiber Bragg filters are useful, but have significant limitations. A primary drawback is that there is only one input direction 506 and one output direction 508, those directions being antiparallel (transmitted signals are not often employed.) This means that a given programmed fiber-grating filter can be configured to produce only a single transfer function in a specific spectral region. Furthermore, a circulator 504, used to separate input and output signals is costly, and adds complexity to the overall device. Finally, programmed fiber Bragg gratings are time-consuming and labor-intensive to fabricate. The transfer function is typically imparted to the material via varying the material's index of refraction along the length of the fiber. Fabrication typically requires complex masking and high power ultraviolet exposure for extended time periods, or complicated ultraviolet holographic exposure apparatus with long exposure times.
There have been filters disclosed comprising systems of uniform diffractive elements, that offer the capability of applying a specific type of spatial wavefront transformation to input signals, but that do not possess the capability of implementing general spatial or spectral transformations. Spatial wavefront transformation capability enhances the capability of the device to accept signals from input ports and map them to outputs ports, without the aid of the auxiliary devices to effect needed spatial wavefront transformations.
There remains a need in the art for spectral filtering devices that offer all of the following features: low cost fabrication, low insertion loss (high efficiency), fully integrated design with no free-space optics, general spatial wavefront transformation capability, general spectral transformation capability, and multiport operation with capability of distinct spectral/temporal and spatial transfer functions connecting operative port pairs.